(1) Field of the Invention
The present invention relates to an image data processing device for reducing a high resolution image which was scanned with an image scanner or with a computer to be outputted with a display device or a video printer as minimizing degradation in-quality and visibility, the display device and the video printer constructed to output a low resolution image.
(2) Description of the Related Art
Recently there has been an increasing demand of an image data processing device to be employed by an image filing system or the like. Receiving binary input data from an image input device such as an image scanner, the image data processing device displays or edits the data, stores the data into an external storage such as a magnetic disc or an optical disc, or outputs the data with a display device or a printing device.
Japanese Patent Publication No. 3-3256 discloses an example of an image processing device including the image data processing device, and this will be described as referring to FIG. 1. The conventional image processing device in FIG. 1 comprises a Central Processing Unit (CPU) 100, a Cathode-Ray Tube (CRT) 101, a main memory 102, an image memory 103, an image scanner 104, a scanner interface (scanner I/F) 105, an external storage 106, and an external storage interface (external storage I/F) 107.
The CPU 100 controls a whole operation of the image processing device.
The CRT 101 works as an image display device.
The main memory 102 is for the CPU 100.
The image memory 103 is for image display use, and operates separately from the main memory 102. That is, the image memory 103 holds image data to be displayed with the CRT 101.
The image scanner 104 scans a paper document.
The scanner I/F 105 transfers binary data of a paper document from the image scanner 104 to the image memory 103.
The external storage 106is comprised of a magnetic disc device or an optical disc device.
The external storage I/F 107 transfers the binary data of the paper document from the image scanner 104 to an external storage 106; further transfers the binary data from the external storage 106 to the image data memory 103 if necessary.
Although the above image processing device has both the main memory 102 and the image memory 103, a general personal computer does not have an image memory 103, and it stores binary data into the main memory 102.
With the above conventional image processing device, the input data scanned with the image scanner 104 is reduced by the CPU 100 to be outputted with the CRT 101. That is, the CPU 100 thins out the input data by removing pixels in the input data at a certain interval; as a result, the whole layout of the input data can be displayed with the CRT 101 at one time. Otherwise, the input data will be displayed part by part, so that only a part of the input data will be displayed at one time. For ease of the description, the former display mode will be referred to as a thin out display mode while the latter display mode will be referred to as a regular display mode hereunder.
Generally the thin out display mode is employed in edition such as displacement, magnification, reduction, and rotation of an image since it provides the user with the whole layout of input data at one time. The thin out reduction is also realized by Or system. It is generally true that binary input data includes less black information than white information. Therefore, in the Or system, binary input data is divided into a number of small cells, and the cell including at least one black pixel is considered as a black pixel.
With the thin out display mode, however, the display loses detail in data, such as characters and lines. Accordingly, data are displayed at the regular display mode if detail in data is needed.
Thus, as for binary input data, visibility is enhanced by the thin out reduction. As for gray-level input data representing continuous tone image, on the other hand, quality level of input data should be sustained while enhancing visibility. When reduced at the thin out reduction mode, gray-level output data will lose detail; accordingly, unwanted visual effects and noise expansion such as moire, granular, and aliasing, will occur. Since such unwanted visual effects and noise expansion .degrade quality of the output image, they should be decreased by the low-pass filtering of the visual system.
The concrete reduction of gray-level input data will be described as referring to figures. For simplifying the description, gray-level input data is one dimensional. At reduction of input data in FIGS. 2(A), (B), and (C), simple average of original pixel levels is computed. Taking FIG. 2(A) as an example, pixels at the upper row constitute original data and pixels at the lower row constitute reduced data. The reduction rate is one-second, in other words pixel level of each pixel at the lower row is computed in accordance with pixel levels of two corresponding pixels at the upper row (K=2). Each arrow shows a reduction operation. That is, pixel level of each pixel at the lower row is computed by multiplying pixel levels of corresponding pixels at the upper row by the coefficient which is equivalent to the reduction rate, then computing the sum of the multiplication results. Thus, the simple average of original pixel levels are computed to obtain a corresponding reduced pixel level. Such simple average can be computed with a low-pass filter. Similarly, in FIG. 2(B) the reduction rate is one-third and a reduced pixel level is obtained by computing a simple average of three original pixels levels. In FIG. 2 (C) the reduction rate is one-fourth and a reduced pixel level is obtained by computing a simple average of four original pixel levels. In the simple average computation, each original pixel is computed just once, and it can be realized with the most simple construction of a digital filter. Also the reduced data do not lose detail in the original data. Further, unwanted visual effects and noise expansion, such as moire, granular, and aliasing are removed by low-pass filtering of the visual system.
Another use of a FIR digital low pass-filter will be described as referring to FIGS. 3(A), (B), and (C). Different from the above, original pixels will be multiplied by weighting coefficients herein. To be noted, the sum of weighting coefficients to be applied to a reduced pixel is always one. Each reduced pixel contains information from some original pixels, and the number of the original pixels is called as a tap number. Generally filter characteristics are enhanced by a larger tap number. The reduction rates in FIGS. 3(A), (B), (C) are half, one-third, and one-fourth respectively. That is, each reduced pixel level is computed from two original pixel levels in FIG. 3 (A) (K=2); each reduced pixel level is computed from three original pixel levels in FIG. 3(B) (K=3); and each reduced pixel level is computed from four original pixel levels in FIG. 3(C) (K=4). In the figures pixels at the upper row constitute original grey-level data while pixels at the lower row constitute reduced grey-level data. Each arrow represents a reduction operation. That is, each pixel level at the lower row is computed by multiplying corresponding pixel levels at the upper row by the coefficient placing on each arrow in the figures; then computing the sum of the multiplication results. The tap number for half reduction rate is 3; and tap numbers and Coefficients for the other reduction rates are determined to realize the substantially same filter characteristics. With half reduction rate in FIG. 3 (A), each reduced pixel includes information from three original pixels. On the other hand, with one-third and one-fourth reduction rates in FIG. 3 (B), (C), each reduced pixel includes information from as many as seven original pixels. It is well known that a larger tap number is required to gain good filter characteristics with a lower cutoff frequency or a cutoff frequency other than a radix fraction, 1/2* of a sampling frequency. With a larger tap number, a larger number of original pixels will be referred in generation of a reduced pixel.
The thin out reduction with weighting coefficients has disadvantages of the complicated computation circuit and the extended run time. That is, a complicated computation circuit is required to realize computation with weighting coefficients. Also an original pixel is employed repeatedly, and this extends the run time. In spite of these disadvantages, the thin out reduction with weighting coefficients is effective in that it realizes the reduction at any reduction rate. That is, input data can be reduced at a reduction rate 1/b even when b is non-integer, by setting a corresponding cutoff frequency.
FIGS. 4 (A), (B), (C) show ideal frequency distributions, frequency distributions at the reduction in FIG. 2, and frequency distributions at the reduction in FIG. 3 respectively. Each figure has a broken line for half reduction rate, a dashed line for one-third reduction rate, and a solid line for one-fourth reduction rate.
Compared to the ideal frequency distributions in FIG. 4(A), the frequencies in FIG. 4(B) has unwanted attenuated frequencies at passing of the filter, and unwanted residual frequencies at rejection with the filter. In the actual display, however, the low-pass filtering significantly enhances the quality of the reduced image. Comparing all the figures with each other, the frequency distributions in FIG. 4 (C) look alike the ideal frequency distributions in FIG. 4 (A) more than the frequency distributions in FIG. 4(B). If the tap number is raised by a larger filter, the frequency distributions will be more ideal than now.
Thus, although the simple average computation in FIG. 2 can be realized with simple construction of the computation circuit, the reduction rate must be expressed by a reduction rate 1/b where b is integer. On the other hand, although the average computation with weighting coefficients in FIG. 3 can realize reduction at any reduction rate, the computation circuit is complicated as well as the run time is extended.
Although input data in the above is one-dimensional, it is generally two-dimensional, such as FIGS. 5(A) and (B). FIG. 5(A) operates simple average computation while FIG. 5(B) operates average computation with weighting coefficients. In FIG. 5(A), a cross-hatched pixel is obtained by reducing the input data including 9 pixels arranged in a matrix of 3 lines and 3 columns. In FIG. 5(B), cross-hatched pixels are obtained by reducing the input data with the filter which is the matrix of 7 lines and 7 columns. To be precise, a weighted mean of 49 pixels is computed. An area including three pixels both in the X and Y directions is subjected to the filter operation, and many of the pixels in the area are referred repeatedly. For example, a pixel can be referred to in at most 9 computations. Accordingly, the circuit realizing the reduction is complicated as well as the run time is extended. Conventionally, when displaying the whole layout of input data scanned from a large document with an image scanner, the input data is thinned .out in accordance with the thin out display mode. With the thin out reduction mode, however, the reduced data loses detail in the input data, such as thin lines. As a result, the visibility of the reduced image decreases by lacking original information, such as characters and diagrams. The visibility of the reduced image can be enhanced by raising the reduction rate. However, the raise of the reduction rate narrows a range of the data which can be displayed. Otherwise, the thin out display mode should be replaced by the regular display mode, so that a part of the data will be displayed in high visibility. Thus, conventionally the whole layout of a large document cannot be displayed at one time without degrading the visibility.
Also reduction of gray-level input data is limited to a reduction rate 1/b where b is integer, even though it achieves high quality of the reduced image. At other reduction rates, on the other hand, weighting average of the pixel levels should be computed instead of simple average thereof. The weighting average requires a complicated computation circuit since weighting coefficients are applied to original pixels; further, it requires long run time since original pixels are referred repeatedly. Also to set a reduction rate of a wide range, a number of computations are required; as a result, the circuit for memorizing coefficients and realizing computations will be more complicated. Along with the increase in the size of the circuit the run time is also extended.